Autonomy for Public Safety
Turning onboard computing into faster situational awareness.
A Drone-as-First-Responder platform integrating EO/IR sensing, onboard computer vision, GIS, and edge AI for firearm detection, crowd awareness, and search-and-rescue missions.
Client
AARD Lab- ERAU
Year
2025– Present
Industry
Public Safety / Autonomous Systems / UAS
Turnaround time
Drone as First Responder, DFR, Firearm Detection, Search and Rescue, Drowning Detection, YOLO, OpenCV, NVIDIA Jetson, EO/IR, Edge AI, GIS, CONOPS, Autonomous UAS
The Challenge
Public-safety UAS need to do more than transmit video. They must rapidly detect threats, locate people, and convert sensor data into information responders can act on.
The Work
I lead multidisciplinary development across two mission areas:
Firearm / Threat Detection
Using YOLO, OpenCV, NVIDIA Jetson, and EO sensors for onboard detection and real-time situational awareness.
Search & Rescue / Drowning Risk
Developing UAS-enabled detection workflows for locating people in water and supporting faster SAR response.
The architecture combines:
EO/IR payloads
NVIDIA Jetson edge compute
YOLO / OpenCV
GIS decision support
autonomous UAS integration
mission-specific CONOPS
What It Means
The goal is to reduce the gap between seeing an event and understanding what responders should do next.
